Literature DB >> 31319078

Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA.

Marc Kohli1, Tarik Alkasab2, Ken Wang3, Marta E Heilbrun4, Adam E Flanders5, Keith Dreyer2, Charles E Kahn6.   

Abstract

Artificial intelligence (AI) will reshape radiology over the coming years. The radiology community has a strong history of embracing new technology for positive change, and AI is no exception. As with any new technology, rapid, successful implementation faces several challenges that will require creation and adoption of new integration technology. Use cases important to real-world application of AI are described, including clinical registries, AI research, AI product validation, and computer assistance for radiology reporting. Furthermore, the informatics technologies required for successful implementation of the use cases are described, including open Computer-Assisted Radiologist Decision Support, ACR Assist, ACR Data Science Institute use cases, common data elements (radelement.org), RadLex (radlex.org), LOINC/RSNA RadLex Playbook (loinc.org), and Radiology Report Templates (radreport.org).
Copyright © 2019 American College of Radiology. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; common data elements; interoperability; machine learning

Year:  2019        PMID: 31319078     DOI: 10.1016/j.jacr.2019.06.009

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  4 in total

1.  Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers.

Authors:  John Mongan; Linda Moy; Charles E Kahn
Journal:  Radiol Artif Intell       Date:  2020-03-25

2.  Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings.

Authors:  Máté E Maros; Chang Gyu Cho; Andreas G Junge; Benedikt Kämpgen; Victor Saase; Fabian Siegel; Frederik Trinkmann; Thomas Ganslandt; Christoph Groden; Holger Wenz
Journal:  Sci Rep       Date:  2021-03-09       Impact factor: 4.379

3.  T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting.

Authors:  J Martijn Nobel; Sander Puts; Jakob Weiss; Hugo J W L Aerts; Raymond H Mak; Simon G F Robben; André L A J Dekker
Journal:  Insights Imaging       Date:  2021-06-10

Review 4.  The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus.

Authors:  Daniele Giansanti; Francesco Di Basilio
Journal:  Healthcare (Basel)       Date:  2022-03-10
  4 in total

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